fixes

Overview

Note
The fixes package currently supports data with annual time intervals only.
For datasets with finer time intervals, such as monthly or quarterly data, I recommend creating a new column with sequential time numbers (e.g., 1, 2, 3, …) representing the time order.
This column can then be used for analysis.

The fixes package is designed for conducting analysis and creating plots for event studies, a method used to verify the parallel trends assumption in two-way fixed effects (TWFE) difference-in-differences (DID) analysis.

The package includes two main functions:

1. run_es(): Accepts a data frame, generates lead and lag variables, and performs event study analysis. The function returns the results as a data frame.
2. plot_es(): Creates plots using ggplot2 based on the data frame generated by run_es(). Users can choose between a plot with geom_ribbon() or geom_errorbar() to visualize the results.

Installation

You can install the package like so:

# install.packages("pak")
pak::pak("fixes")

or

install.packages("fixes")

If you want to install development version, please install from GitHub repository:

pak::pak("yo5uke/fixes")

How to use

First, load the library.

library(fixes)

Data frame

The data frame to be analyzed must include the following variables:

1. A variable to identify individuals.
2. A dummy variable indicating treated individuals (e.g., is_treated).
3. A variable representing time (e.g., year).
4. An outcome variable.

For example, a data frame like the following:

run_es()

run_es() takes 11 arguments, including required variables and optional specifications like covariates and clustering.

Example: Without Covariates

event_study <- run_es(
  data       = df, 
  outcome    = y, 
  treatment  = is_treated, 
  time       = year, 
  timing     = 1998, 
  lead_range = 5, 
  lag_range  = 5, 
  fe         = ~ firm_id + year, 
  cluster    = ~ state_id, 
  baseline   = -1, 
  interval   = 1
)

Note: The fe argument must be specified as a one-sided formula (e.g., ~ firm_id + year).
The cluster argument can be specified either as a one-sided formula (e.g., ~ state_id) or as a character vector (e.g., c("firm_id", "year")).

The run_es() function returns a tidy data frame with estimated event-study coefficients, confidence intervals, and metadata such as relative timing and baseline identification¹.

Example: With Covariates

event_study <- run_es(
  data       = df, 
  outcome    = y, 
  treatment  = is_treated, 
  time       = year, 
  timing     = 1998, 
  lead_range = 5, 
  lag_range  = 5, 
  covariates = ~ cov1 + cov2 + cov3, 
  fe         = ~ firm_id + year, 
  cluster    = ~ state_id, 
  baseline   = -1, 
  interval   = 1
)

You can use this result to create custom plots, or take advantage of the built-in plot_es() function to visualize the estimates and confidence intervals with minimal code.

plot_es()

The plot_es() function creates a plot based on ggplot2.

plot_es() has 12 arguments.

If you don’t care about the details, you can just pass the data frame created with run_es() and the plot will be complete.

plot_es(event_study)

plot_es(event_study, type = "errorbar")

plot_es(event_study, type = "errorbar", vline_val = -.5)

Since it is created on a ggplot2 basis, it is possible to modify minor details.

plot_es(event_study, type = "errorbar") + 
  ggplot2::scale_x_continuous(breaks = seq(-5, 5, by = 1)) + 
  ggplot2::ggtitle("Result of Event Study")

Planned Features

• Support for custom confidence level in plot_es() (e.g., conf_level = 0.90)
• Support for faceted plots by subgroup (e.g., facet_by = "group")

Debugging

If you find an issue, please report it on the GitHub Issues page.